Multi-objective optimization in highway pavement maintenance and rehabilitation project selection and scheduling:A state-of-the-art review

The motivation for cost-effective management of highway pavements is evidenced not only by the massive expenditures associated with these activities at a national level but also by the consequences of poor pavement condition on road users.This paper presents a state-of-the-art review of multi-objective optimization(MOO)problems that have been formulated and solution techniques that have been used in selecting and scheduling highway pavement rehabilitation and maintenance activities.First,the paper presents a taxonomy and hierarchy for these activities,the role of funding sources,and levels of jurisdiction.The paper then describes how three different decision mechanisms have been used in past research and practice for project selection and scheduling(historical practices,expert opinion,and explicit mathematical optimization)and identifies the pros and cons of each mechanism.The paper then focuses on the optimization mechanism and presents the types of optimization problems,formulations,and objectives that have been used in the literature.Next,the paper examines various solution algorithms and discusses issues related to their implementation.Finally,the paper identifies some barriers to implementing multi-objective optimization in selecting and scheduling highway pavement rehabilitation and maintenance activities,and makes recommendations to overcome some of these barriers.

Assessment of Farmer’s Practices on the Rubber Nursery Management Using Growing Medium and Varietal Selection in Mon State

Rubber is an economically important perennial crop in Myanmar for latex production. As the rubber plantation area increases yearly, the requirement of vigorous rubber seedlings for its establishment plays a central role. The success of rubber plantations depends on some farming practices such as using different compost or other materials in the potting soil-medium, varietal selection for stock and scion in the budding process, and fertilizer application methods. The objective of this study was to assess the farmer’s practices in the establishment of rubber nurseries in mostly rubber planted areas in Myanmar. The survey interviewed 60 respondents from three townships in Mon State, namely Mudon, Kyaikmaraw, and Thanbyuzayat. The response data were analyzed through the descriptive method. This survey exposed the potentially active operators (middle age of 30 - 60 years) in rubber nursery production. Local experienced farmers usually raised the budded seedlings with 15 cm × 23 cm polyethylene bag in all study regions. Most farmers selected multi-clonal seed for stock and BPM 24 for scion according to the local market demand and high latex yield. All survey areas used both organic and inorganic fertilizers for nutrient management. Compost is a chief component of growing medium in their nursery production. However, making compost and high demand of the compost were local constraints. Therefore, this survey suggested improving the proper composting method for rubber nursery establishment.

Effects of single-tree selection harvesting on hymenopteran and saproxylic insect assemblages in the canopy and understory of northern temperate forests

Insects respond to changes in microhabitat caused by canopy disturbance, and thus can be used to examine the ecological impacts of harvesting. Single-tree selection harvesting is the most common silvicultural system used to emulate local small-scale natural disturbance and maintain uneven-aged forest structure in temperate forests. Here, we test for differences in richness, abundance, and composition of hymenopteran and saproxylic insect assemblages at four different taxon levels （selected insect orders; and all hymenopteran families, and braconid subfamilies and morphospecies） between the canopy and understory of unharvested and single-tree selection harvested sites in a northern temperate forest from central Canada. Harvesting had no effect on insect assemblage richness, composition or abundance at the three highest taxon levels （order, family and subfamily）. Similarly, richness and abundance at the lowest-taxon level （braconid morphospecies） were similar, although composition differed slightly between unharvested and harvested stands. Insect assemblages were vertically stratified, with generally higher abundance （for Diptera, Hymenoptera, some hymenopteran families and braconid subfamilies） and richness （for braconid morphospecies） in the understory than the canopy. In particular, composition of the braconid morphospecies assemblage showed relatively low similarity between the understory and canopy. Single-tree selection harvesting appears to influence wood-associated insect taxa only subtly through small changes in community composition at the lowest taxon level, and thus is recommended as a conservative approach for managing these northern temperate forests.

Kentish Plover(Charadrius alexandrinus) and Little Tern(Sternula albifrons) prefer shells for nesting:A field experiment

Shorebird populations are declining worldwide,mainly due to human disturbances and loss of coastal wetlands.However,supratidal habitats as saltpans could play a role in buffering human impact.Saltpans have shown to be important as feeding or breeding sites of some shorebird species.A potential conservation strategy to increase shorebird populations in saltpans is to manipulate the cues that birds use to select optimal breeding habitat.Here it is hypothesized that shorebirds are attracted to bivalve shells due to the advantages they offer.Following this hypothesis,we supplemented a restored saltpan in 2019 and 2021 with bivalve shells,expecting an increase in the number of breeding birds’ nests.More than 75% of Kentish Plover(Charadrius alexandrinus) and Little Tern(Sternula albifrons) nests were found in patches with shells in both years.The best model for both species indicates that the presence of shells is the factor that most correlates with the location of nests.The probability of choosing one place over another to settle their nest increases in areas with an abundance of shells,double in the case of the Kentish Plover and triple in the case of the Little Tern.The result of this study may constitute a valuable tool for attracting birds to restored saltpans and could contribute to the success of expensive restoration projects where time is usually a constraint.

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

Adaptive path selection for the improved distributed mobility management

Current mobility management schemes usually represent centralized or hierarchical architectures,which force data traffic to be processed by a centralized mobility anchor.This allows the mobile node(MN)to be reachable anywhere and provides an efficient method for seamless session continuity.However,all of the signal messages and data traffic converge on particular mobility anchor,which causes excessive signaling and traffic at the centralized mobility anchor and single point of failure issues as data traffic increases.To overcome these limitations and handle increasing data traffic,the distributed mobility management(DMM)scheme has emerged as an alternative solution.Although previous researches have been conducted on DMM support,because their schemes employ an unconditional way to make direct paths after handover,they have some drawbacks,such as several signaling and chain of tunneling problems.Therefore,this paper introduces a new DMM scheme which adaptively creates a direct path.To support it,we present the path selection algorithm,which selects the most efficient path between a direct path and no direct path based on routing hops and traffic load.Through the performance analysis and results,we confirm that the proposed scheme is superior in terms of signaling and packet delivery costs.

Management of lateral pelvic lymph nodes in rectal cancer:Is it time to reach an Agreement?

In this editorial,we proceed to comment on the article by Chua et al,addressing the management of metastatic lateral pelvic lymph nodes(mLLN)in stage II/III rectal cancer patients below the peritoneal reflection.The treatment of this nodal area sparks significant controversy due to the strategic differences followed by Eastern and Western physicians,albeit with a higher degree of convergence in recent years.The dissection of lateral pelvic lymph nodes without neoadjuvant therapy is a standard practice in Eastern countries.In contrast,in the West,preference leans towards opting for neoadjuvant therapy with chemoradiotherapy or radiotherapy,that would cover the treatment of this area without the need to add the dissection of these nodes to the total mesorectal excision.In the presence of high-risk nodal characteristics for mLLN related to radiological imaging and lack of response to neoadjuvant therapy,the risk of lateral local recurrence increases,suggesting the appropriate selection of strategies to reduce the risk of recurrence in each patient profile.Despite the heterogeneous and retrospective nature of studies addressing this area,an international consensus is necessary to approach this clinical scenario uniformly.

China Procurement-Leveraging Supplier Selection and Supplier Management for Success

Successful China pro- curement is the result of a comprehensive set of complex and high-ly-specialized processes. Dis- tilling this down to a ＂magical recipe＂ for success is no simple task, and there are no shortcuts. However, as a useful reference, two fundamental ingredients for procurement success are： the selection of the right suppliers： and the effective management of chosen suppliers to optimize their performance.

Comparison on neural networks and support vector machines in suppliers' selection

Suppliers＇ selection in supply chain management （SCM） has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statistical methods. However, neural networks have inherent drawbacks, such as local optimization solution, lack generalization, and uncontrolled convergence. A relatively new machine learning technique, support vector machine （SVM）, which overcomes the drawbacks of neural networks, is introduced to provide a model with better explanatory power to select ideal supplier partners. Meanwhile, in practice, the suppliers＇ samples are very insufficient. SVMs are adaptive to deal with small samples＇ training and testing. The prediction accuracies for BPNN and SVM methods are compared to choose the appreciating suppliers. The actual examples illustrate that SVM methods are superior to BPNN.

Path Selection in Disaster Response Management Based on Q-learning

Suitable rescue path selection is very important to rescue lives and reduce the loss of disasters, and has been a key issue in the field of disaster response management. In this paper, we present a path selection algorithm based on Q-learning for disaster response applications. We assume that a rescue team is an agent, which is operating in a dynamic and dangerous environment and needs to find a safe and short path in the least time. We first propose a path selection model for disaster response management, and deduce that path selection based on our model is a Markov decision process. Then, we introduce Q-learning and design strategies for action selection and to avoid cyclic path. Finally, experimental results show that our algorithm can find a safe and short path in the dynamic and dangerous environment, which can provide a specific and significant reference for practical management in disaster response applications.

Test selection and optimization for PHM based on failure evolution mechanism model

The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.

Supplier Selection Fuzzy Model in Supply Chain Management:Vietnamese Cafe Industry Case

Supplier selection is a vital part of the supply chain and is also a current issue that concerns businesses today as supplier quality directly affects the operations of the organization.Choosing the right supplier can help businesses increase productivity,competitiveness in the market,and profits without having to lower the quality of the products.However,choosing a supplier is not a simple matter,it requires businesses to consider many aspects about their suppliers.Therefore,the goal of this study is to propose an integrated model consisting of two models:Fuzzy Analytics Network Process(Fuzzy-ANP)model and Weighted Aggregated Sum Product Assessment(WASPAS)to solve the problem above.The Fuzzy-ANP model was developed to evaluate the weightings of the supplier selection criteria,and the WASPAS Model was used to rank the suppliers.An example of supplier selection in the coffee industry in Vietnam was studied to validate the model,namely 5 main criteria,with 16 sub-criteria,and 7 suppliers.The model test results show that the Fuzzy ANP and WASPAS integration model was suitable.In future,these developing models can apply to other industries or integrate with other models.

Sanitary Landfill Site Selection Using Multi-criteria Analysis Evaluation:Case Study of Dibra Region,Albania

Landfill is a common solution for the final disposal of MSW(Municipal Solid Waste)in Albania.Landfill sitting is an extremely difficult task to accomplish because the site selection process depends on different factors and regulations.To ensure that an appropriate site is chosen,a systematic process should be developed and followed.In this study,10 candidate sites for an appropriate landfill area in Dibra Region are determined by using the MCE(Multi-criteria Evaluation).From the application of the exclusion criteria provided in the study methodology,it was able to find the best three alternatives.The statistical processing for the determination of the best place was accomplished through MCA(Multi-criteria Analysis)and Environmental Management,for three scenarios with different weights of criteria.The application of this method has led to the identification of the most suitable site for the construction of sanitary landfill in the Dibra Region.

Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational sector.The LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning experience.The recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for LMS.Among the different processes involved in ML based LMS,feature selection and classification processesfind beneficial.In this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for LMS.The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS.The pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal features.Besides,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)algorithm.The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance.The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects.The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

Intelligent Student Mental Health Assessment Model on Learning Management System

A learning management system(LMS)is a software or web based application,commonly utilized for planning,designing,and assessing a particular learning procedure.Generally,the LMS offers a method of creating and delivering content to the instructor,monitoring students’involvement,and validating their outcomes.Since mental health issues become common among studies in higher education globally,it is needed to properly determine it to improve mental stabi-lity.This article develops a new seven spot lady bird feature selection with opti-mal sparse autoencoder(SSLBFS-OSAE)model to assess students’mental health on LMS.The major aim of the SSLBFS-OSAE model is to determine the proper health status of the students with respect to depression,anxiety,and stress(DAS).The SSLBFS-OSAE model involves a new SSLBFS model to elect a useful set of features.In addition,OSAE model is applied for the classification of mental health conditions and the performance can be improved by the use of cuckoo search optimization(CSO)based parameter tuning process.The design of CSO algorithm for optimally tuning the SAE parameters results in enhanced classifica-tion outcomes.For examining the improved classifier results of the SSLBFS-OSAE model,a comprehensive results analysis is done and the obtained values highlighted the supremacy of the SSLBFS model over its recent methods interms of different measures.

“监督机制”还是“掏空行为”:超额聘任独立董事与企业环保投资

以2007—2021年沪深A股上市公司为研究对象,深入探究超额聘任独立董事对企业环保投资的影响效应及作用机制。研究发现:超额聘任独立董事对企业环保投资具有显著的促进作用,验证了超额聘任独立董事属于一种“监督机制”;超额聘任独立董事通过抑制管理层短视和大股东掏空行为来促进企业进行环保投资;在重污染行业、两职合一以及独立董事薪酬较低的企业中,超额聘任独立董事对企业环保投资的促进作用更显著。

高血压患者规范管理与家医签约一体化的现况调查

目的了解北京市月坛地区高血压患者进行家医签约的一般现状,探索高血压管理与家医签约的整合现况,为提高高血压管理效率、促进家医签约服务的可持续发展探索切合社区实际工作的方法。方法采用现况调查的方法,对2022年6—8月在月坛社区卫生服务中心进行电子签约家医服务的高血压患者进行问卷调查。问卷包括基本情况、高血压诊疗服务及需求、高血压知识知晓情况及家医签约服务满意度。结果在签约家医服务的高血压患者中,失眠和使用降压药物种类对血压的控制差异有统计学意义(P<0.05)。高血压患者对高血压相关用药知识的知晓率为20.45%,对其危险因素的知晓率为43.18%,对其并发症的知晓率为33.64%。高血压患者且对诊疗预约服务和微信服务有较大需求。高血压患者对原有家医签约服务满意度得分的中位数为93分。结论应积极控制高血压患者的失眠情况及使用降压药物的种类,充分利用家医签约服务微信群及时增强诊疗预约和健康教育能力,促进高血压管理与家医签约的充分融合。

Strategies for Cultivating Public Funded Targeted Normal Students Based on Goal Management Theory : A Case Study of East, West and North Guangdong

Public funded targeted normal students are an important component of China's teacher team construction.Since its implementation in 2007,a large number of outstanding rural teachers who have been striving on the front line of education have been trained.Based on the theory of goal management,this paper explores the problems and countermeasures in the training of public funded targeted normal students.It strives to solve the problems of low willingness to teach and high default rates among public funded normal students,and hopes that the suggestions proposed in this paper can further promote the effective implementation of policies for public funded normal students.